plotly

Generate interactive Plotly visualizations from structured datasets in Python.

3|Updated Apr 2, 2026

Search Tags:#analytics #charts #data #interactive #visualization #plotting

Ready to run

npx skills add https://github.com/legout/pi-config --skill plotly-legout

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: plotly
Source: https://github.com/legout/pi-config/tree/main/installed-skills/plotly
Command: npx skills add https://github.com/legout/pi-config --skill plotly-legout

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires plotly, pandas, kaleido, dash, and includes references (resource) components.

What problem does it solve?

This skill solves the challenge of creating complex, interactive, and professional-grade data visualizations in Python without needing to master low-level graphics libraries.

Core Features & Use Cases

Interactive Visualization: Generate charts that support zooming, panning, and hover-tooltips out of the box.
Versatile Charting: Create over 40 types of visualizations, including 3D plots, financial charts, geographic maps, and statistical distributions.
Use Case: Use this skill to build a multi-trace dashboard for financial analysis or to generate high-resolution scientific plots for research papers.

Quick Start

Use the plotly skill to generate an interactive scatter plot from the provided dataframe df with x and y axes defined.

Frequently Asked Questions about plotly

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create interactive data visualizations in Python without learning low-level graphics libraries?▼

Interactive data visualizations can be generated using the Plotly library for Python, which supports zooming, panning, and hover-tooltips out of the box. It renders interactive HTML or static images from structured datasets without requiring low-level graphics programming.

Can I build financial charts and 3D plots for data analysis using Plotly?▼

Yes, Plotly supports over 40 types of visualizations for data analysis, including 3D plots, financial charts, geographic maps, and statistical distributions. You can build multi-trace dashboards for financial analysis or generate high-resolution scientific plots.

What Python packages do I need to render interactive HTML and static images from a dataframe?▼

Rendering interactive HTML and static images requires the plotly package, along with pandas for structured datasets and kaleido for static image export. The dash package is also included as a dependency for building interactive analytical dashboards.

What's the best way to generate publication-quality scientific plots from structured datasets?▼

The best way to generate publication-quality scientific plots is using Plotly to create high-resolution visualizations from structured datasets. It supports a wide range of scientific and statistical chart types, rendering results as interactive HTML or static images suitable for research papers.

Does interactive charting with Plotly work with pandas dataframes for automated reporting?▼

Yes, interactive charting with Plotly works directly with pandas dataframes for automated reporting. You can generate interactive scatter plots and other visualizations by defining x and y axes from your dataframe, producing HTML outputs for interactive analysis.

Why use Plotly over other Python data visualization libraries for complex charts?▼

Plotly is used over other Python data visualization libraries because it creates complex, interactive, and professional-grade charts without needing to master low-level graphics libraries. It provides built-in interactivity like zooming and tooltips across over 40 chart types, including 3D and geographic visualizations.